Learning Machine Learning with Aston

This week the machine learning group and I explored Decision Trees and I also continued work on the paper airplanes project, meeting Dr. Hassibi to discuss our research plan at Caltech as well. Decision Trees: After completing the Concept Map on Linear Regression (my subconcept was the derivative), the group and I looked into Decision Trees through the David Fumo Medium articles. The first article revolved around the iris dataset and using decision trees to classify an iris into one of three species. Having most of my coding experience either just with the Runestone Textbook or Codeacademy's platform, I was initially very confused as to what to do logistically (how to work PyCharm, pip install, etc). Observing and asking group mates though, I soon began to feel accustomed to the general process and realized that even if I didn't have as strong a foundation, I was here because my personality enabled me to learn, catch up, and excel. After the iris decision tree, we start...

First weekly journal! Getting a head start on our Paper Airplane Lab #2, Elizabeth and I did the experimentation for our second lab to optimize plane design for sustainability. We tested this time the Hammer design with the full sheet of paper and quarter sheet of paper. Compared to Lab 1 though, the folding was harder for the Hammer due to much thicker buildups of paper. I also got back my Lab Write Up 1 and learned some of the strengths and weaknesses I had and I revised it for Sunday's revision writeup. This week, we had our first official Caltech visit with the professors! Though it was only about an hour, I learned a lot from Dr. Hassibi. He tested the waters to see what we already knew but really made an impact through his connections to real world situations. While we had been learning about the math or research process for machine learning, Dr. Hassibi's connection of it to identifying dog breeds or personality quizzes reinforced my fascination with the field....

Through these several weeks, we have begun our journey to exploring research in machine learning by starting our paper airplane research project, hearing undergraduate research presentations, and learning the foundations of machine learning and the basic principles of research.  Foundations: Our first several weeks covered the foundations of research and more specifically machine learning. In a class activity, all the groups came up with several truths about research and the class was able to collectively develop a set of rules and expectations for the research journey. From "always keep on trying" to "look to nature for inspiration," many of these truths resounded with me as I hope to learn more about this field via research in this course.  The foundations of machine learning were also covered through a series of videos. From explaining neural networks to linear regression, these videos established a baseline from which my group and I could work o...